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Article

Remote Worker Communication Technology Use Related to Role Clarity, Coworker Support, and Work Overload

by
Inyoung Shin
1,
Sarah E. Riforgiate
2,*,
Emily A. Godager
2 and
Michael C. Coker
3
1
Department of Computer Science, Yale University, New Haven, CT 06520, USA
2
Department of Communication, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
3
Department of Communication, Boise State University, Boise, ID 83725, USA
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(7), 2830; https://doi.org/10.3390/su17072830
Submission received: 21 January 2025 / Revised: 17 March 2025 / Accepted: 18 March 2025 / Published: 22 March 2025
Browse Figure
Versions Notes

Abstract

:
Building upon the job demands-resources model, this study examines how communication technology (CT) use in remote work relates to role clarity, coworker support, work overload, and, in turn, burnout to enhance sustainable worker and organizational workplace practices. By analyzing non-experimental survey data from 447 U.S. workers transitioning to remote work in 2020, we found that job demands/resources mediated the relationships between CTs and burnout, with each CT linked to specific demands/resources. Phone calls, email, and instant messaging were associated with role clarity and coworker support, mitigating burnout. Video calls were linked to higher work overload and increased burnout which can decrease worker and organizational sustainability. Our findings highlight the importance of CT use in relation to employee well-being. Supported by affordance theory, we found that each CT had unique associations with job resources and demands when CTs served as key communication channels during organizational transitions.

1. Introduction

Disruptions to work arrangements due to natural disasters, pandemics, and other crises require organizations and workers to leverage communication technologies (CTs; i.e., mobile telephones, mobile texts, emails, video calls, and instant messaging apps) to continue work efforts and cope with abrupt changes [1]. These disruptions threaten worker and organizational sustainability when workers must grapple with managing stress and burnout during rapid work adaptations [2]. One such event occurred when the COVID-19 pandemic spread worldwide [3], and workers rapidly shifted from on-site work to remote arrangements, which created significant stressors including disrupted work patterns, increased ambiguity regarding work performance and responsibilities, and disconnected work relationships [4,5]. As a result, Sigahi, Yeow, and Thatcher [6] urged for further study of information technology systems that workers and organizations rely on to determine intervention points to increase sustainability. During the COVID-19 pandemic, workers inevitably relied on CTs to cope with the abrupt changes, and this unique situation presented an opportunity to examine how mediated communication either contributed to or restricted workers’ ability to manage their stress and burnout when shifting to remote work.
The impact of CTs on workers’ well-being is more complex than the simple positive or negative dichotomy, with research acknowledging both positive and negative effects of using CTs in work settings [7,8]. Excessive CT use while working remotely increases workers’ burnout levels through information overload [9], work interruptions [10], pressure to work after hours or be constantly available [11,12], and technostress, which occurs when individuals experience psychological distress related to technology adaptations [12,13]. Conversely, remote work offers opportunities for efficient and flexible communication among workers [14], increasing productivity and efficiency [13].
These contrasting effects of CTs reported in the research can be explained by the job demands-resources (JD-R) model, suggesting that CT use is simultaneously associated with both job demands and job resources [15]. The JD-R model refers to resources and demands arising from workplace rather than individual factors, including how workers formally and informally communicate information, manage tasks and organizational work processes, and maintain relationships with other colleagues [2,16]. The JD-R model was developed to emphasize the operational and social dimensions of work related to workers’ well-being. The model highlights how CT use enhances and constrains employee well-being because it affects various aspects of workplace interactions and processes.
During organizational changes and crises, workers must adapt to new expectations and navigate shifting dynamics [5,17,18]. Communication is critical for managing stressors during such times, with CT playing a vital role in (re)shaping job resources and addressing job demands, especially when office-based communication is disrupted [1]. Research has related CT use with circumstantial aspects of job resources and demands, such as constant connectivity and increased interruptions during routine work [15,19]. However, despite the importance of CTs, there is limited research on the role of specific CTs in shaping core workplace dynamics during crises from a JD-R perspective.
The shift to remote work during the COVID-19 pandemic was a crisis that necessitated immediate work rearrangement and adaptation, threatening workers’ and organizational sustainability [2]. A spectrum of CTs facilitated communication among work associates and created new communication workflows during the crisis [18]. Building on this context, this study explores how CT use relates to job demands/resources central to workplace functioning such as work overload, organizing work, exchanging information and knowledge (i.e., role clarity), and fostering interpersonal relationships (i.e., coworker support).
This study also explores nuanced CT use in the JD-R model by narrowing the investigation to five specific CTs that were widely adopted across industries: mobile telephones, mobile texts, emails, video calls (e.g., Zoom), and instant messaging apps (e.g., Slack). Departing from a rich research history focused on the impact of broad CT use as a generalized and combined construct [15,20], we take an exploratory approach to understand how specific CT types relate to different JD-R outcomes. According to the technology affordance framework [21], workers perceive the possibilities or constraints of technologies differently depending on the CT type [22]. Thus, JD-R outcomes likely vary by CT type as different affordances are associated with each CT.
Drawing upon a non-experimental survey of 447 U.S. workers who abruptly shifted from on-site to remote work across numerous industries, we examined how mobile telephones, mobile texts, emails, video calls, and instant messaging apps were associated with burnout and if these relationships were mediated by work overload, role clarity, and coworker support to contribute to sustainable workplace practices. This study focuses on the United States context because the federal government and many state governments mandated remote work during the COVID-19 pandemic [23]. However, the findings have broader implications regarding how CTs help individual workers manage job resources and demands during crises and/or when work routines are disrupted more broadly.

2. Backgrounds

2.1. Burnout and the JD-R Model

Workers inevitably experience stress related to their job tasks and work relationships [2,24]. Although some work stress is necessary to induce high energy and motivation, prolonged and repeated stress exposure results in employee burnout, a state of emotional depletion and decreased job motivation [25]. Research consistently indicates that burnout can result from employees’ personal issues and/or incompetence, as well as organizational problems including ineffective leadership, flawed workflows, or impersonal work environments [26]. Ultimately, workers’ perceptions of work conditions are critical to workers’ burnout levels.
The JD-R model [16] provides insights into work-related factors that influence employees’ workplace perceptions, particularly related to burnout, by emphasizing two essential elements: job demands and job resources. Job demands refer to job-related risk factors (e.g., high workloads, time pressures, emotional demands, and role ambiguity) that require considerable physical or emotional effort to manage [27]. Job resources encompass physical, psychological, or social aspects of the job and workplace features that increase work efficiency and worker motivation [24], and they are found across organization levels, including broad organizational structures (e.g., salary, career opportunities, and job security), the organization of work (e.g., role clarity), organizing interpersonal and social communication (e.g., coworker support), and the nature of the task itself (e.g., autonomy and significance) [16]. Workers experience burnout when job demands are high and when job resources are limited [16].
Mixed findings on the positive and negative effects of CTs on burnout can be explained through the JD-R model. While extensive evidence indicates that CT use enhances productivity [14], workplace CT use can also increase job demands related to information overload [9], pressure to be constantly available [11,12], and work–life conflict [12,19,28,29]. Ter Hoeven et al. [15] examined these contrasting effects of CTs in one research context by focusing on the mediating roles of job demands/resources specific to the technological aspects of CT use, such as unpredictability, interruption, accessibility, and efficiency. Their findings indicate that CT use can have both positive and negative impacts on worker well-being depending on how the opposing effects of job demands and resources interact. During the COVID-19 pandemic, when CTs served as the primary workplace communication method [30], CT uses likely influenced the perceptions of work conditions [31]. The collective research pertinent to CTs and burnout leads us to posit that job demands/resources mediate the relationship between CT use and burnout.
Although previous research identifies numerous job demands and resources as potential contributors to burnout [27], this study focuses specifically on work overload, role clarity, and coworker support, which are tied to core work dynamics such as workflows, information exchange, and interpersonal interactions [16]. The contextual factors of organizational changes heighten both the importance and the impact of these job demands and resources on worker burnout, which can threaten worker and organizational sustainability. During the COVID-19 pandemic, workers faced increased workloads, changes to routines and processes, and an intensified need for clear guidance on their responsibilities [17,32]. At the same time, coworker support became crucial for maintaining stability and alleviating uncertainty during the transition to remote work [28,33]. Especially when changes require sudden physical separation, as seen during the COVID-19 pandemic, CT use among colleagues becomes essential for addressing processes tied to these job demands and resources [18].

2.2. Work Overload, CTs, and Burnout

Work overload occurs when individuals face excessive work volume and/or challenges that exceed their mental and/or physical capabilities [25]. Meta-analyses conducted on the JD-R model [26,27] consistently emphasize work overload as one of the most significant precursors to burnout. Work overload is also reported to be a major factor increasing the risk of burnout during remote work [34,35].
CT use can be related to burnout partly because it increases workload. Frequent CT use may signal increased workload, particularly if CTs are utilized for specific tasks [18]. Excessive asynchronous communication contributes to piled-up tasks and information to manage, potentially overwhelming individuals [9,36]. The pressure to respond immediately to colleagues through CTs, coupled with the absence of face-to-face interactions and delays in sending/receiving responses to resolve issues, can also exacerbate workloads [37]. Especially during the COVID-19 pandemic, workers experienced the abrupt replacement of face-to-face interactions with CTs, indicating a disruption of practices and inducing work overload. CT use during this period could lead to information overload and difficulties in time and task management. Thus, we hypothesize the following:
H1: 
frequent CT use is indirectly associated with employee burnout through perceived work overload.

2.3. Role Clarity, CTs, and Burnout

Role clarity and its opposite, role ambiguity, is the job demand/resource derived from communicating information about roles and responsibilities. Role clarity occurs when individuals understand expectations and feel they have accurate information about their responsibilities at work [34]. Role clarity is enhanced when organizations share sufficient information with workers and efficiently organize their work [8]. Employees with high role clarity can collaborate effectively with others [34], ultimately reducing work-related stress. Conversely, role ambiguity (absence of role clarity) has been identified as a main cause of employee burnout [27].
Remote work contexts are typically marked by the absence of on-site communication. Remote workers encounter challenges observing co-workers [32], attaining supervisor recognition [38], and/or delineating role boundaries [39]. These challenges necessitate more frequent and transparent communication of roles and expectations [40].
Remote work research yields mixed findings regarding the impact of mediated communication via CTs on role clarity. While media richness theory [41] raises doubts about the efficiency of mediated communication, recent studies report varied results. Without on-site communication, workers inevitably become proactive in using CTs to acquire crucial information regarding their duties, tasks, and expectations [42]. Chewning et al., [43] found that CTs facilitated relevant information exchanges within/between community organizations during natural disasters when in-person communication channels were interrupted. Similarly, Yang et al. [44] found that during the COVID-19 pandemic, remote workers in a large technology organization frequently utilized asynchronous CTs like emails and instant messaging to access information about altered work arrangements. Whillans et al. [18] discovered both positive and negative aspects of specific CTs related to role clarity/ambiguity. Asynchronous communication, like Slack messaging, had a limited ability to convey high-quality information, potentially increasing role ambiguity [18]. However, while synchronous communication, like video calls, was demanding, it also helped workers process role and task information, potentially increasing role clarity [18]. Although the direction and relationship between CT use and role clarity remain unclear, role clarity is related to CT use, which leads us to propose the following:
H2: 
frequent CT use is indirectly related to employee burnout through role clarity.

2.4. Coworker Support, CTs, and Burnout

Coworker support is the assistance, encouragement, and emotional backing that colleagues provide to one another in the workplace [45]. The JD-R model suggests that coworker support motivates employees and allows them to address job demands more effectively [16]. Informal communication among employees is important for establishing and maintaining emotionally supportive workplace relationships (e.g., hallway and water cooler chats) [32,46]. However, remote workers inevitably depend on CTs to mobilize coworker support. Relying on CTs can present a significant challenge for giving/receiving support, resulting in less developed workplace relationships in remote work contexts [47]. Workers’ supportive communication experiences via CTs are mixed. Negative experiences include feeling isolated [48], uncertain, or unable to predict support during work interruptions [15]. Alternatively, workers can view CTs positively as a tool to help manage supportive, relational communication [28,48].
As CTs have become more commonplace, ample research indicates that organizational members successfully use various CTs (e.g., phone, email, instant messaging, and (enterprise) social media) to exchange support [49,50]. Remote workers are also more motivated to use CTs to connect with coworkers to compensate for the absence of supportive face-to-face interactions [32]. Whether positive or negative, using CTs in remote work environments provides access to coworker support [4]. Therefore, we hypothesize the following:
H3: 
frequent CT use is related to employee burnout through perceived coworker support.

2.5. CT Types Related to JD-R

Several CTs including email, phones (including voice calls and text messages), and online instant messaging services are prevalent across organizations [51]. Video calling and conferencing via Zoom or Microsoft Teams became more prevalent for remote work communication arrangements during the COVID-19 pandemic [52]. Although the extent to which each CT type is used varies by workers and organizations, remote workers typically use at least five types of CTs—phone calls, text messages, emails, video calls, and instant messages—to perform work and foster work relationships [53].
Each CT is likely to relate to job demands/resources differently, as the effects of CTs vary based on CT features and users’ relationship to them [21,46]. Affordance theory [21] is an extensively cited framework that accounts for the variations in the types of CT uses and impacts. Affordances refer to the perceived and actual properties of an object that determine how it can be used [21]. Users generally perceive the affordance of a CT based on its actual features, previous experiences, observations of others’ use, and/or social norms around its use. Affordances guide users in understanding what actions are possible and how they can use the technology [54]. While affordances for each CT may be enacted differently by individuals and their workplaces [55], exploring shared and general affordances related to each CT helps generate theoretical and practical guidance about specific CT uses in workplaces [22] and can contribute to sustainable worker and employee practices. As such, our inquiry into how specific CTs relate to the JD-R is broadly guided by the affordance framework as well as existing research on specific affordance categories [56].
Phone calls and video calls (e.g., Zoom and Microsoft Teams) are often associated with affordances such as high levels of social presence, synchronicity, and bandwidth (i.e., the breadth of communicative cues potentially conveyed through a channel) [56]. CTs with such affordances allow workers to ask questions, seek clarification, receive instant feedback from coworkers in real time, and transmit rich cues that clarify meaning and reduce misinterpretation [57]. Additionally, these affordances can often create intimate contexts that promote emotional expression and understanding, strengthening the sense of closeness [58]. However, simultaneously, high levels of social presence, synchronicity, and bandwidth can be intrusive, which can disrupt workflows and increase cognitive workloads [59].
Asynchronous, text-based CTs such as text messages, instant messaging, and emails are commonly used on mobile devices that workers carry throughout the day [60]. Asynchronous CTs are often grouped by similar affordances, such as accessibility and conversation control [44], which allow users to exchange messages without time, place, or other structural barriers, facilitating timely communication that contributes to higher role clarity [36,61]. Ironically, these affordances can also create pressure to remain constantly connected or tethered to work [62]. Furthermore, asynchronous CTs offer written, persistent communication logs, enabling workers to reference previous communications for their tasks. However, these logs can also lead to a task buildup, such as unanswered emails, which can exacerbate work overload.
Traditionally, asynchronous text-based CTs have been found to decrease co-worker connections because they are limited in fostering an informal and supportive atmosphere [41]. However, mobile texts and instant messages are well suited for short exchanges, which can promote informal and conversational information sharing [48]. A case study on instant messaging platforms, such as Slack, indicated that teams often use these CTs to engage in more casual and fun communication compared to traditional work channels [63].
While we anticipate distinct relationships between different types of CTs and job resources based on their unique affordances, it is crucial to note that affordances for each CT are context-dependent and not always fixed [55]. Instead of focusing on isolating and quantifying affordances as separate constructs [56], our study takes a more practical approach by examining how distinctions between different CTs relate to job resources and demands. This approach reflects the complexity of measuring affordances directly and aligns better with the goals and context of our research, leading to our research question:
RQ 1: 
how does each type of CT uniquely relate to role clarity, coworker support, and (remote) work overload?

3. Methods

3.1. Participant Procedures and Characteristics

In response to the COVID-19 pandemic, the United States Federal government issued a Stay-at-Home Order in March 2020 [23]. This unprecedented measure provided an appropriate research context for us to conduct a non-experimental study to examine the associations between CT use at work, job resources (i.e., role clarity and coworker support), demands (i.e., coworker support), and workers’ well-being during a transition to remote work. We conducted an online survey from 29 April to 15 May 2020, directly following the Stay-at-Home Order issuance, to reach individuals in the United States who moved to remote work conditions and maintained work relationships and responsibilities through CT interactions. (During the United States Stay-at-Home Order in response to the COVID-19 pandemic, some work continued on site for individuals deemed essential workers (i.e., medical facilities), but other industries stopped work operations completely (i.e., restaurants), and a majority of organizations used technology to continue at least some work from workers’ homes (i.e., education and technology). This study recruited the third group of workers who continued working by transitioning from working at the organization to working remotely using technology.)
Participation was limited to full-time workers who transitioned from on-site to remote work due to the COVID-19 pandemic. Given the time constraints during this crisis, we employed two participant recruitment methods: (1) Amazon MTurk, an online crowdsourcing site (71.7% of participants), and (2) the researchers’ email and social media networks (29.3% of participants). Although our methods did not involve a random sampling representative survey, they (particularly MTurk) allowed for expedient data collection within a short time compared to other online recruitment methods [64,65] while maintaining relative reliability and validity. Moreover, MTurk data have been acknowledged for not distorting the view of the United States population [64]. MTurk participants received a $1.50 participation incentive, while social network participants were not compensated.
Participants were required to be at least 18 years old, reside in the United States, work full-time (over 30 h per week), and have transitioned from on-site to remote work following the COVID-19 pandemic Stay-at-Home Order. We used several safeguards to ensure that the overall data were valid. To allow only one survey attempt for each participant, we blocked multiple survey attempts from the same IP address. MTurk provides its own metric called the “MTurk approval rate” to evaluate the quality of its participants based on their previous participation. Following the recommended practice [66], we required MTurk participants to have at least a 90% MTurk approval rating to receive a survey link. We ensured each participant’s eligibility by including screening questions about age, residency, average working hours per week, and workplace transition during the COVID-19 pandemic. The screening procedures before the main survey allowed us to recruit 601 adults over 18 years old who worked more than 30 h remotely in the United States and transitioned to remote work due to the social distancing order. We ensured the accuracy and validity of survey answers by inserting open-ended questions and attention checks at several points in the survey [67]. Only 447 of the 601 participants provided valid responses; about 22% of participants failed at least one attention check or showed unusual answers to open-ended questions, so their responses were excluded from the analysis.
Participants’ average age was 36.6 years (SD = 10.7). About 49% of participants identified themselves as male, 50.8% as female, and 0.2% as another gender not listed. Participants were white (75.7%), Black or African American (11%), and other races such as Asian or Native American (13.3%). United States participants reported living in 47 different states, with 12.3% of participants answering that they lived in California, followed by Kansas (9.4%), Texas (7.8%), Wisconsin (6.9%), and Florida (6.3%). (Details on participants’ resident states are reported in the Supplementary Material Section in Table S1.)
On average, participants had completed college with 16.2 years of formal education (SD = 1.64). Annual household income was reported in ranges, with 16% of participants earning under $50,000, 56.7% earning between $50,000 and $70,000, and 27.3% earning more than $70,000. Participants worked in 16 industries, with the largest concentrations in technology (22.1%) and education (18.6%). (Details on participants’ occupational industry fields are reported in the Supplementary Material Section in Table S2.) Considering management positions, 43.4% reported working as employees rather than as managers; a total of 36% reported working as first-level managers, and 20.6% reported working as higher-level managers. The average employment length in participants’ current positions was 6.5 years (SD = 10.2). Regarding working hours after the transition from on-site to remote work, only 10.3% reported spending more time working; a total of 42.7% reported no change, and 47% reported working fewer hours (M = −5.27 and SD = 10.17).

3.1.1. Employee Burnout

We measured participants’ burnout levels using a reduced set of 11 items [68] from the Maslach Burnout Inventory [25]. Example items include: “I feel used up at the end of the workday”, “I feel frustrated by my job”, and “I have accomplished many worthwhile things in this job” (reverse code). The final burnout score was calculated by averaging participants’ responses to those 11 items with 5-point Likert scales (0 = strongly disagree, 4 = strongly agree, M = 1.97, SD = 0.88, Cronbach’s α = 0.841, and McDonald’s ω total = 0.901). Confirmation factor analysis (CFA) confirmed the validity of combining the items (CFI = 0.998, TLI = 0.995, and RMSEA = 0.043).

3.1.2. Role Clarity

We measured perceived role clarity using the role ambiguity scale developed by Singh and Rhoads [69]. We asked participants how often they felt a series of six statements related to role ambiguity (i.e., “I have clear, planned goals and objectives for my job” and “I know what my responsibilities are”) using 5-point scales (0 = never and 4 = always). The final role clarity score was computed by averaging participants’ responses on the six items (M = 2.91, SD = 0.74, Cronbach’s α = 0.833, and McDonald’s total ω = 0.856). CFA confirmed the validity of combining the items (CFI = 0.987, TLI = 0.974, and RMSEA = 0.069).

3.1.3. Coworker Support

We determined perceived coworker support through Caplan et al.’s social support scale [70]. Participants answered three items: “I can rely on my coworkers for help when things get tough at my work”, “My coworkers are willing to listen to my job-related problems”, and “My coworkers are easy to talk to”. We used a 5-point Likert scale (0 = strongly disagree and 4 = strongly agree) and averaged participants’ responses across the three items (M = 2.80, SD = 1.0, Cronbach’s α = 0.828, and McDonald’s total ω = 0.835). EFA confirmed the validity of combining the items.

3.1.4. Work Overload

We measured remote work overload by modifying information and communication technology workload scales developed by Day et al. [71]. Participants answered three questions using a 5-point Likert scale (0 = strongly disagree and 4 = strongly agree): “Remote work creates more work for me”, “As a result of remote work, I work longer hours than before”, and “Remote work increases my workload”. We averaged participants’ responses such that a higher score indicated a higher perceived remote workload (M = 1.86, SD = 1.20, Cronbach’s α = 0.800, and McDonald’s total ω = 0.877). EFA confirmed the validity of combining the items.

3.1.5. CT Use

Informed by Anderson and Vogels’s report [72] of commonly used CTs during the COVID-19 pandemic, we measured five different types of CT use based on participants’ self-reporting. We asked participants how frequently they used (mobile) phone calls, mobile texts, video calls or conferencing (e.g., Zoom and Microsoft Teams), email services, and instant messages via apps (e.g., Slack) to communicate with their coworkers each day in the previous month. Replies were recorded on a 5-point scale (0 = never, 1 = less than once a day, 2 = once a day, 3 = few times a day, and 4 = all the time). Participants’ CT use included the following: (mobile) phone calls (M = 2.50 and SD = 1.21), mobile texts (M = 2.59 and SD = 1.24), video calls or conferencing (M = 2.55 and SD = 1.16), email services (M = 3.24 and SD = 0.92), and instant messages (M = 2.72 and SD = 1.26).

3.2. Analysis

Based on SPSS macro process 3.5 [73], we performed a series of ordinary least squares (OLS) regression analyses to specify a path model incorporating the direct and indirect relationships between role clarity, coworker support, work overload, CT use, and employee burnout. In our analysis, we controlled for participants’ socioeconomic status (i.e., sex, age, race, yearly household income, and years of education). Additionally, because our study examines burnout experienced by individual workers, we included participants’ working and organizational conditions relevant to burnout (i.e., current workplace tenure, length of time in current role, and change in working hours during the COVID-19 pandemic). To test the significance of indirect relationships in the model, we used the 5000 bootstrapped, biased-corrected re-sample approach provided SPSS macro process 3.5 [73].

4. Results

The path analysis results are reported in Table 1 and Figure 1. The test results for the significance of indirect relationships are reported in Table 2. Drawing upon the JD-R model and previous research on workplace CT use, we proposed that job demands/resources, including role clarity, coworker support, and work overload, would mediate the relationship between CTs and burnout. Consistent with our anticipation, there was no direct relationship between CT use and burnout, except for phone calls. Frequent use of phone calls was directly related to lower burnout (b = 0.072 and p < 0.001).
Regarding H1, we found that work overload was related to higher burnout (b = 0.076 and p < 0.001). However, we only found that frequent video call use was related to greater remote work overload (b = 0.658 and p < 0.05). Based on the bootstrapping method, we verified that work overload mediated the relationship between video call use and burnout (b = 0.050, SE = 0.014, LLCI = 0.024, and ULCI = 0.081). Thus, H1 was partially supported.
Consistent with research on JD-R, we found a negative relationship between role clarity and burnout (b = −0.391 and p < 0.001). This relationship was substantive; the standard coefficient for role clarity had the highest value among other job demand/resource variables ( β = −0.327). This result partially supports H2; role clarity mediated the relationship between some CT use and burnout based on a bootstrapping method. Specifically, frequent uses of phone calls (b = −0.054, SE = 0.015, LLCI = −0.088, and ULCI = −0.027) and emails (b = −0.034, SE = 0.018, LLCI = −0.071, ULCI = −0.0003) were associated with lower levels of burnout through higher levels of role clarity.
We also found evidence to partially support H3, as some CT use was significantly associated with lower burnout through coworker support. Specifically, coworker support was associated with lower burnout (b = −0.144 and p < 0.001), and the use of instant messaging was associated with higher coworker support (b = 0.104 and p < 0.01). A bootstrapping method verified that instant messaging was indirectly related to burnout through coworker support (b = −0.016, SE = 0.008, LLCI = −0.031, and ULCI = −0.002).
Responding to RQ1, we found that CTs were selectively associated with role clarity, coworker support, and remote work overload. As discussed above, among the five types of CTs used by participants with their coworkers, phone calls (b = 0.138 and p < 0.001) and emails (b = 0.088 and p < 0.05) were associated with higher role clarity. We additionally found that phone calls ( β = 0.227) had a more substantive relationship to role clarity than emails ( β = 0.111). Only one type of CT—instant messaging—was associated with coworker support. Similarly, only one type of CT—video calls—was related to work overload.

5. Discussion

5.1. Research Contributions

In this study, our objective was to test three hypotheses regarding CT use at work, job demands and resources, and employee burnout to determine how CT use relates to employee well-being during times of disruption. Drawing upon the analysis of non-experimental survey data from 448 remote workers, we found partial support for our hypotheses, suggesting that some CT uses with work associates during the early phase of shifting to emergency remote work were associated with either high or low burnout levels, depending on their relationship with work overload (H1), role clarity (H2), and coworker support (H3). This aligns with previous research on the mediating role of JD-R variables between CT use and worker well-being [15]. We also expanded on the results from hypotheses testing by exploring the research question on the relationship between different types of CTs and job demands and resources. Our findings demonstrate that CT use was particularly relevant to key JD-R variables, including role clarity, coworker support, and work overload. These findings emphasize the need for CT-related policies and interventions to ensure that CTs are used effectively to address core work dynamics (e.g., work processes), organizational communication, and interpersonal relationship, especially when routine communication is disrupted during organizational transitions to enhance sustainable workplace practices. Overall, our research objective was achieved.
Moreover, our study highlights different associations between specific CT use and job demand/resources. While our study occurred during the COVID-19 pandemic, our work offers relevant support and extension of existing research. Previous studies that identified and disentangled the affordances of CTs guided our interpretation of these findings. For example, phone calls often provide real-time feedback and clarification, which help streamline work processes and clarify role assignments [44,74]. This aligns with our finding that phone calls are positively associated with role clarity and lower burnout. Video calls (e.g., Zoom) have been considered as conveying more bandwidth cues than phone calls [56]. However, we found that video calls were linked to higher work overload. This may be because the broad range of communication cues in video calls did not offer straightforward benefits [75]. Additional communication cues conveyed through video calls can require greater focus and attention to interpret the cues. Furthermore, due to their affordances, video calls may be used for more complex work tasks, such as collaborative work, that require processing a wealth of information and inherently increase workloads. These distinct findings for phone calls and video calls suggest a non-linear relationship between affordances, CTs, and job demands/resources.
Our findings also imply different affordances among asynchronous CTs like email, instant messaging, and mobile texting during remote work, especially when workers abruptly transitioned during the COVID-19 pandemic. Only emails, which typically enable access to detailed and persistent information [39], were positively related to role clarity. This suggests that during a crisis with rapid adjustments to workflow processes, emails offer an organizationally sanctioned formal channel that allows employees to search and revisit pertinent information to enhance role clarity.
We found that instant messaging was associated with higher coworker support rather than role clarity. Instant messaging is known to be suitable for informal interactions and supportive communication among peers [48,63], and our findings confirm and extend these affordances. However, despite the recognized benefits of mobile texting in personal communication [76], we found no significant relationship between mobile texting and coworker support. Texting is often viewed as personal and not typically supported by organizations. In remote work settings, using texting to communicate with coworkers might be perceived as intrusive to personal boundaries rather than a resource conduit [30].

5.2. Practical Implications

Based on our findings, we offer practical insights for organizations transitioning to remote work and encouraging worker CT adoption that leads to more sustainable practices. Initially, organizations can enhance access to job resources by supporting various modes of mediated communication during remote work implementation. For example, using emails for detailed and documented asynchronous communication, as well as employing phone calls for tasks requiring immediate feedback, enables organizations to capitalize on the benefits associated with CT affordances. Fostering informal communication among coworkers could be achieved by providing dedicated organizational channels, such as instant messaging platforms. However, organizations need to be mindful of the non-linear relationship between the affordances and benefits of using CTs, acknowledging that the same affordance may act as a constraint depending on the context. In our analysis, the extensive transmission of information through video calls could overwhelm workers, underscoring the importance of thoughtful consideration of communication breadth. Also, employees may hesitate to use CTs, particularly those endorsed by organizations, due to concerns about organizational surveillance [77].

6. Limitations and Future Directions

While our study captured CT use during a crisis situation which necessitated the rapid move to remote work, we acknowledge the findings need to be interpreted considering the following limitations. First, we recognize that social desirability bias is a limitation of survey-based data. To minimize bias, we asked participants to respond honestly, emphasized that there were no right or wrong answers, assured them that their responses were completely anonymous, and communicated that the survey was voluntary.
Second, while we intentionally designed our study to capture different CTs, our work did not directly identify and measure specific affordances for each CT. Our study explored different patterns in CT use and relevant outcomes based on the assumption that these tools afford different possibilities for action, but we cannot precisely specify what those actions are. Additionally, CTs do not possess fixed affordances [55]. Our findings may not be universally applicable to contexts where workers are already accustomed to remote work. Researchers and practitioners should note that the ways workers perceive and utilize CT affordances are evolving alongside changing remote work conditions, necessitating continual refinement of understanding how affordances are enacted in practice.
Furthermore, our data were cross-sectional. Although Harman’s single-factor test indicated that common method bias was not a significant issue (According to exploratory factor analysis, the total variance explained by the first factor was 26.3%.) and JD-R variables are often treated as mediators in studies on communication technology (CT) use [4,15,31], our statistical analysis does not establish causality between CT use and JD-R variables. We cannot completely exclude the possibility that job demands/resources established before remote work moderated the effects of CT use on burnout in other contexts. Future longitudinal studies are necessary to increase our understanding of the causal relationships between CT use and JD-R variables in the context of burnout.
Additionally, our study recruited diverse workers from numerous organizations and fields in the United States, allowing us to examine workers’ perceptions of CT use and burnout. However, because we controlled for organizational factors in our analysis, our study cannot explore the influences of macro-organizational structures or cultures on remote worker well-being. Future research can extend our work by recruiting participants within specific organizations, as well as exploring organizations in other international contexts. Alternatively, extending Miglioretti et al.’s work [8] examining the influences of various organizational factors, such as structures or employment situations, on CT use in remote work settings could offer insightful understanding for remote workers’ well-being.
Moreover, our study focused only on role clarity, worker support, and work overload. However, we found that phone calls were directly related to lower levels of burnout without mediation from the three types of JD-R in our model. This suggests that other types of job demands/resources may be associated with CTs that were not examined in this study. Future research should explore additional JD-R types related to CT use and employee well-being.
Finally, as an exploratory approach, our study did not consider other aspects of communication beyond frequency. Conducting a content analysis on the types of information that workers communicate through CTs and with whom could deepen our understanding of the roles of CT use in remote work settings. Despite these limitations, our study demonstrates how mediated communication via various CTs can facilitate or constrain access to job demands/resources during transitional periods to remote work and influence worker and organizational sustainable practices. Our research approach and insights can guide future studies exploring the roles of CTs and their affordances during organizational shifts and more specifically in remote work settings.

7. Conclusions

When organizations were required to move to remote work arrangements during the COVID-19 pandemic, there was an initial concern that the unprepared, fully remote environment would pose challenges to coordinating work and managing interpersonal dynamics, potentially burdening individual workers [4]. However, we found that the frequent use of some CTs (e.g., phone calls, emails, and instant messaging) were related to higher role clarity and coworker support, which correlated to lower worker burnout. Although our study did not directly compare remote work with fully co-located situations, findings suggest that remote work does not necessarily worsen work conditions. Instead, the stress levels in remote work environments can vary depending on how CTs are integrated into core work dynamics. In other words, CTs can serve as tools to support workers’ adaptability in addressing sudden organizational changes when co-location is not possible [1,28,43] and therefore contribute to workplace sustainability. While the COVID-19 pandemic recedes, many organizations have continued remote work arrangements [53,78], integrating remote with co-located work. Continuing to investigate the expanded and contradictory roles of CTs in hybrid situations is an important future research direction.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17072830/s1, Table S1: Participant resident state; Table S2: Participant occupation by industry.

Author Contributions

Conceptualization, I.S., S.E.R., E.A.G. and M.C.C.; methodology, I.S. and S.E.R.; formal analysis, I.S.; resources I.S. and S.E.R. data curation, I.S. and S.E.R.; writing—original draft preparation, I.S., S.E.R., E.A.G. and M.C.C.; writing—review and editing I.S., S.E.R., E.A.G. and M.C.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of University of Wisconsin-Milwaukee (protocol code 20.301 on 28 April 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets presented in this article are not readily available because the data are part of an ongoing study, and the participant consent form did not indicate that data would be distributed beyond study publications.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Donnelly, N.; Proctor-Thomson, S.B. Disrupted Work: Home-Based Teleworking (HbTW) in the Aftermath of a Natural Disaster. New Technol. Work Employ. 2015, 30, 47–61. [Google Scholar] [CrossRef]
  2. Dima, G.; Meseșan Schmitz, L.; Șimon, M.-C. Job Stress and Burnout among Social Workers in the VUCA World of COVID-19 Pandemic. Sustainability 2021, 13, 7109. [Google Scholar] [CrossRef]
  3. Kruyen, P.; André, S.; Van der Heijden, B. Maintaining a Sustainable Work–Life Balance: An Interdisciplinary Path to a Better Future; Edward Elgar Publishing: Northampton, MA, USA, 2024. [Google Scholar]
  4. van Zoonen, W.; Sivunen, A.; Blomqvist, K.; Olsson, T.; Ropponen, A.; Henttonen, K.; Vartiainen, M. Understanding stressor–strain relationships during the COVID-19 pandemic: The role of social support, adjustment to remote work, and work–life conflict. J. Manag. Organ. 2021, 27, 1038–1059. [Google Scholar] [CrossRef]
  5. Paula, V.M.d.; Teles, J.; Cotrim, T.P. Organizational and Individual Factors Influencing the Quality of Working Life Among Brazilian University Professors during COVID-19. Sustainability 2024, 16, 6351. [Google Scholar] [CrossRef]
  6. Sigahi, T.F.A.C.; Yeow, P.H.P.; Thatcher, A. Advancing Sustainability in the Future of Work through the Design of Post-Pandemic Work-from-Home Systems. Sustainability 2023, 15, 15367. [Google Scholar] [CrossRef]
  7. Foley, M.; Cooper, R.; Vromen, A.; Lee, T.; Tapsell, A. ‘Everything now, all the time’: The connectivity paradox and gender equality in the legal profession. New Technol. Work Employ. 2024, 39, 362–381. [Google Scholar]
  8. Miglioretti, M.; Gragnano, A.; Simbula, S.; Perugini, M. Telework quality and employee well-being: Lessons learned from the COVID-19 pandemic in Italy. New Technol. Work Employ. 2023, 38, 548–571. [Google Scholar] [CrossRef]
  9. Cho, J.; Lee, H.E.; Kim, H. Effects of communication-oriented overload in mobile instant messaging on role stressors, burnout, and turnover intention in the workplace. Int. J. Commun. 2019, 13, 21. [Google Scholar]
  10. Cheng, X.; Bao, Y.; Zarifis, A. Investigating the impact of IT-mediated information interruption on emotional exhaustion in the workplace. Inf. Process. Manag. 2020, 57, 102281. [Google Scholar]
  11. Xie, J.; Ma, H.; Zhou, Z.E.; Tang, H. Work-related use of information and communication technologies after hours (W_ICTs) and emotional exhaustion: A mediated moderation model. Comput. Hum. Behav. 2018, 79, 94–104. [Google Scholar] [CrossRef]
  12. Kim, K.H.; Chon, M.-G. When work and life boundaries are blurred: The effect of after-hours work communication through communication technology on employee outcomes. J. Commun. Manag. 2022, 26, 386–400. [Google Scholar] [CrossRef]
  13. Farmania, A.; Elsyah, R.D.; Fortunisa, A. The Phenomenon of Technostress during the COVID-19 Pandemic Due to Work from Home in Indonesia. Sustainability 2022, 14, 8669. [Google Scholar] [CrossRef]
  14. Pokojski, Z.; Kister, A.; Lipowski, M. Remote Work Efficiency from the Employers’ Perspective—What’s Next? Sustainability 2022, 14, 4220. [Google Scholar] [CrossRef]
  15. Ter Hoeven, C.L.; van Zoonen, W.; Fonner, K.L. The practical paradox of technology: The influence of communication technology use on employee burnout and engagement. Commun. Monogr. 2016, 83, 239–263. [Google Scholar] [CrossRef]
  16. Bakker, A.B.; Demerouti, E. The Job Demands-Resources model: State of the art. J. Manag. Psychol. 2007, 22, 309–328. [Google Scholar] [CrossRef]
  17. Verlinden, S.; Wynen, J.; Kleizen, B.; Verhoest, K. Blurred Lines: Exploring the Impact of Change Complexity on Role Clarity in the Public Sector. Rev. Public Pers. Adm. 2022, 43, 479–503. [Google Scholar] [CrossRef]
  18. Whillans, A.; Perlow, L.; Turek, A. Experimenting during the shift to virtual team work: Learnings from how teams adapted their activities during the COVID-19 pandemic. Inf. Organ. 2021, 31, 100343. [Google Scholar] [CrossRef]
  19. Wright, K.B.; Abendschein, B.; Wombacher, K.; O’Connor, M.; Hoffman, M.; Dempsey, M.; Krull, C.; Dewes, A.; Shelton, A. Work-Related Communication Technology Use Outside of Regular Work Hours and Work Life Conflict:The Influence of Communication Technologies on Perceived Work Life Conflict, Burnout, Job Satisfaction, and Turnover Intentions. Manag. Commun. Q. 2014, 28, 507–530. [Google Scholar] [CrossRef]
  20. Fonner, K.L.; Roloff, M.E. Testing the Connectivity Paradox: Linking Teleworkers’ Communication Media Use to Social Presence, Stress from Interruptions, and Organizational Identification. Commun. Monogr. 2012, 79, 205–231. [Google Scholar] [CrossRef]
  21. Gibson, J. The theory of affordances. In Perceiving, Acting and Knowing: Towards an Ecological Psychology; Erlbaum Associates: Hillsdale, NJ, USA, 1977. [Google Scholar]
  22. Evans, S.K.; Pearce, K.E.; Vitak, J.; Treem, J.W. Explicating Affordances: A Conceptual Framework for Understanding Affordances in Communication Research. J. Comput.-Mediat. Commun. 2016, 22, 35–52. [Google Scholar] [CrossRef]
  23. Moreland, A. Timing of state and territorial COVID-19 stay-at-home orders and changes in population movement—United States, March 1–May 31, 2020. MMWR Morb. Mortal. Wkly. Rep. 2020, 69, 1198–1203. [Google Scholar] [CrossRef]
  24. Riforgiate, S.E.; Howes, S.S.; Simmons, M.J. The Impact of Daily Emotional Labor on Health and Well-Being. Manag. Commun. Q. 2022, 36, 391–417. [Google Scholar] [CrossRef]
  25. Maslach, C.; Jackson, S.E.; Leiter, M.P. Maslach Burnout Inventory; Scarecrow Education: Lanham, MD, USA, 1997. [Google Scholar]
  26. Lee, R.T.; Ashforth, B.E. A meta-analytic examination of the correlates of the three dimensions of job burnout. J. Appl. Psychol. 1996, 81, 123. [Google Scholar] [PubMed]
  27. Alarcon, G.M. A meta-analysis of burnout with job demands, resources, and attitudes. J. Vocat. Behav. 2011, 79, 549–562. [Google Scholar]
  28. Lott, Y.; Abendroth, A.-K. Affective commitment, home-based working and the blurring of work–home boundaries: Evidence from Germany. New Technol. Work Employ. 2023, 38, 82–102. [Google Scholar]
  29. Shin, I.; Riforgiate, S.E.; Coker, M.C.; Godager, E.A. Communication Technology and Social Support to Navigate Work/Life Conflict During COVID-19 and Beyond. Manag. Commun. Q. 2023, 37, 686–707. [Google Scholar] [CrossRef]
  30. Pennington, N.; Holmstrom, A.J.; Hall, J.A. The Toll of Technology while Working from Home during COVID-19. Commun. Rep. 2022, 35, 25–37. [Google Scholar]
  31. Wang, H.; Liu, P.; Zhao, X.; Li, A.; Xiao, C. Work-Related Use of Information and Communication Technologies After Hours (W_ICTs) and Work-Family Conflict: A Moderated Mediation Model. Sage Open 2022, 12, 21582440221120169. [Google Scholar]
  32. Woo, D.; Endacott, C.G.; Myers, K.K. Navigating Water Cooler Talks Without the Water Cooler: Uncertainty and Information Seeking During Remote Socialization. Manag. Commun. Q. 2023, 37, 251–280. [Google Scholar]
  33. Wang, W.; Albert, L.; Sun, Q. Employee isolation and telecommuter organizational commitment. Empl. Relat. Int. J. 2020, 42, 609–625. [Google Scholar]
  34. Mihalca, L.; Lucia Ratiu, L.; Brendea, G.; Metz, D.; Dragan, M.; Dobre, F. Exhaustion while teleworking during COVID-19: A moderated-mediation model of role clarity, self-efficacy, and task interdependence. Oeconomia Copernic. 2021, 12, 269–306. [Google Scholar] [CrossRef]
  35. Parra, C.M.; Gupta, M.; Cadden, T. Towards an understanding of remote work exhaustion: A study on the effects of individuals’ big five personality traits. J. Bus. Res. 2022, 150, 653–662. [Google Scholar]
  36. Barley, S.R.; Meyerson, D.E.; Grodal, S. E-mail as a Source and Symbol of Stress. Organ. Sci. 2011, 22, 887–906. [Google Scholar]
  37. Ayoko, O.B.; Konrad, A.M.; Boyle, M.V. Online work: Managing conflict and emotions for performance in virtual teams. Eur. Manag. J. 2012, 30, 156–174. [Google Scholar]
  38. Cristea, I.C.; Leonardi, P.M. Get Noticed and Die Trying: Signals, Sacrifice, and the Production of Face Time in Distributed Work. Organ. Sci. 2019, 30, 552–572. [Google Scholar] [CrossRef]
  39. Erhardt, N.; Gibbs, J.; Martin-Rios, C.; Sherblom, J. Exploring Affordances of Email for Team Learning Over Time. Small Group Res. 2016, 47, 243–278. [Google Scholar]
  40. Shockley, K.M.; Allen, T.D.; Dodd, H.; Waiwood, A.M. Remote worker communication during COVID-19: The role of quantity, quality, and supervisor expectation-setting. J. Appl. Psychol. 2021, 106, 1466–1482. [Google Scholar] [CrossRef]
  41. Daft, R.L.; Lengel, R.H. Organizational Information Requirements, Media Richness and Structural Design. Manag. Sci. 1986, 32, 554–571. [Google Scholar]
  42. Marlow, S.L.; Lacerenza, C.N.; Salas, E. Communication in virtual teams: A conceptual framework and research agenda. Hum. Resour. Manag. Rev. 2017, 27, 575–589. [Google Scholar]
  43. Chewning, L.V.; Lai, C.-H.; Doerfel, M.L. Organizational Resilience and Using Information and Communication Technologies to Rebuild Communication Structures. Manag. Commun. Q. 2013, 27, 237–263. [Google Scholar]
  44. Yang, L.; Holtz, D.; Jaffe, S.; Suri, S.; Sinha, S.; Weston, J.; Joyce, C.; Shah, N.; Sherman, K.; Hecht, B.; et al. The effects of remote work on collaboration among information workers. Nat. Hum. Behav. 2022, 6, 164. [Google Scholar] [CrossRef] [PubMed]
  45. Beehr, T.A.; King, L.A.; King, D.W. Social support and occupational stress: Talking to supervisors. J. Vocat. Behav. 1990, 36, 61–81. [Google Scholar] [CrossRef]
  46. Haythornthwaite, C. Social networks and Internet connectivity effects. Inf. Commun. Soc. 2005, 8, 125–147. [Google Scholar] [CrossRef]
  47. Vayre, E.; Pignault, A. A systemic approach to interpersonal relationships and activities among French teleworkers. New Technol. Work Employ. 2014, 29, 177–192. [Google Scholar] [CrossRef]
  48. Jämsen, R.; Sivunen, A.; Blomqvist, K. Employees’ perceptions of relational communication in full-time remote work in the public sector. Comput. Hum. Behav. 2022, 132, 107240. [Google Scholar]
  49. Huang, L.V.; Liu, P.L. Ties that work: Investigating the relationships among coworker connections, work-related Facebook utility, online social capital, and employee outcomes. Comput. Hum. Behav. 2017, 72, 512–524. [Google Scholar] [CrossRef]
  50. Leidner, D.E.; Gonzalez, E.; Koch, H. An affordance perspective of enterprise social media and organizational socialization. J. Strateg. Inf. Syst. 2018, 27, 117–138. [Google Scholar] [CrossRef]
  51. D’Urso, S.C.; Pierce, K.M. Connected to the Organization: A Survey of Communication Technologies in the Modern Organizational Landscape. Commun. Res. Rep. 2009, 26, 75–81. [Google Scholar]
  52. Leonardi, P.M. COVID-19 and the new technologies of organizing: Digital exhaust, digital footprints, and artificial intelligence in the wake of remote work. J. Manag. Stud. 2021, 58, 249. [Google Scholar]
  53. Parker, K.; Horowitz, J.M.; Minkin, M. How the Coronavirus Outbreak Has—And Hasn’t—Changed the Way Americans Work. Pew Res. Cent. 2020, 9, 1–32. [Google Scholar]
  54. Norman, D.A. Affordance, conventions, and design. Interactions 1999, 6, 38–43. [Google Scholar]
  55. Orlikowski, W.J. Using Technology and Constituting Structures: A Practice Lens for Studying Technology in Organizations. Organ. Sci. 2000, 11, 404–428. [Google Scholar] [CrossRef]
  56. Fox, J.; McEwan, B. Distinguishing technologies for social interaction: The perceived social affordances of communication channels scale. Commun. Monogr. 2017, 84, 298–318. [Google Scholar]
  57. Wu, Y.; Sun, Y.; Sundar, S.S. What Do You Get from Turning on Your Video? Effects of Videoconferencing Affordances on Remote Class Experience During COVID-19. Proc. ACM Hum.–Comput. Interact. 2022, 11, 1–21. [Google Scholar] [CrossRef]
  58. Bleakley, A.; Rough, D.; Edwards, J.; Doyle, P.; Dumbleton, O.; Clark, L.; Rintel, S.; Wade, V.; Cowan, B.R. Bridging social distance during social distancing: Exploring social talk and remote collegiality in video conferencing. Hum.–Comput. Interact. 2022, 37, 404–432. [Google Scholar]
  59. Waizenegger, L.; McKenna, B.; Cai, W.; Bendz, T. An affordance perspective of team collaboration and enforced working from home during COVID-19. Eur. J. Inf. Syst. 2020, 29, 429–442. [Google Scholar]
  60. Schrock, A.R. Communicative affordances of mobile media: Portability, availability, locatability, and multimediality. Int. J. Commun. 2015, 9, 18. [Google Scholar]
  61. Cheng, B.H.; Zhou, Y.; Chen, F. You’ve got mail! How work e-mail activity helps anxious workers enhance performance outcomes. J. Vocat. Behav. 2023, 144, 103881. [Google Scholar]
  62. Leonardi, P.M.; Treem, J.W.; Jackson, M.H. The Connectivity Paradox: Using Technology to Both Decrease and Increase Perceptions of Distance in Distributed Work Arrangements. J. Appl. Commun. Res. 2010, 38, 85–105. [Google Scholar]
  63. Bunce, M.; Wright, K.; Scott, M. ‘Our newsroom in the cloud’: Slack, virtual newsrooms and journalistic practice. New Media Soc. 2018, 20, 3381–3399. [Google Scholar]
  64. Sheehan, K.B. Crowdsourcing research: Data collection with Amazon’s Mechanical Turk. Commun. Monogr. 2018, 85, 140–156. [Google Scholar]
  65. Boas, T.C.; Christenson, D.P.; Glick, D.M. Recruiting large online samples in the United States and India: Facebook, Mechanical Turk, and Qualtrics. Political Sci. Res. Methods 2020, 8, 232–250. [Google Scholar]
  66. Nadler, J.; Baumgartner, S.; Washington, M. MTurk for Working Samples: Evaluation of Data Quality 2014–2020. N. Am. J. Psychol. 2021, 23, 741–752. [Google Scholar]
  67. Chmielewski, M.; Kucker, S.C. An MTurk Crisis? Shifts in Data Quality and the Impact on Study Results. Soc. Psychol. Personal. Sci. 2020, 11, 464–473. [Google Scholar]
  68. Rutherford, B.N.; Shepherd, C.D.; Tashchian, A. Validating the reduced burnout scale and sequencing of burnout. J. Bus. Res. 2015, 68, 67–73. [Google Scholar]
  69. Singh, J.; Rhoads, G.K. Boundary Role Ambiguity in Marketing-Oriented Positions: A Multidimensional, Multifaceted Operationalization. J. Mark. Res. 1991, 28, 328–338. [Google Scholar]
  70. Caplan, R.D.; Cobb, S.; French, J.R. Relationships of cessation of smoking with job stress, personality, and social support. J. Appl. Psychol. 1975, 60, 211–219. [Google Scholar]
  71. Day, A.; Paquet, S.; Scott, N.; Hambley, L. Perceived information and communication technology (ICT) demands on employee outcomes: The moderating effect of organizational ICT support. J. Occup. Health Psychol. 2012, 17, 473–491. [Google Scholar]
  72. Anderson, M.; Vogels, E. Americans Turn to Technology During COVID-19 Outbreak, Say an Outage Would Be a Problem; Pew Research Center: Washington, DC, USA, 2020. [Google Scholar]
  73. Hayes, A.F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach; Guilford Publications: New York, NY, USA, 2017. [Google Scholar]
  74. Hacker, J.; Vom Brocke, J.; Handali, J.; Otto, M.; Schneider, J. Virtually in this together—How web-conferencing systems enabled a new virtual togetherness during the COVID-19 crisis. Eur. J. Inf. Syst. 2020, 29, 563–584. [Google Scholar]
  75. Bailenson, J.N. Nonverbal overload: A theoretical argument for the causes of Zoom fatigue. Technol. Mind Behav. 2021, 2, 1–6. [Google Scholar]
  76. Ling, R.; Bertel, T.F.; Sundsøy, P.R. The socio-demographics of texting: An analysis of traffic data. New Media Soc. 2012, 14, 281–298. [Google Scholar] [CrossRef]
  77. Allen, M.W.; Coopman, S.J.; Hart, J.L.; Walker, K.L. Workplace surveillance and managing privacy boundaries”: Erratum. Manag. Commun. Q. 2008, 21, 418. [Google Scholar]
  78. Gallup. Indicator: Hybrid Work. 2022. Available online: https://www.gallup.com/401384/indicator-hybrid-work.aspx (accessed on 11 November 2024).
Sustainability 17 02830 g001
Figure 1. Results of the path model. Controls are omitted; only significant paths are shown; unstandardized coefficients are indicated; *** < 0.001, ** < 0.01, * < 0.05.
Figure 1. Results of the path model. Controls are omitted; only significant paths are shown; unstandardized coefficients are indicated; *** < 0.001, ** < 0.01, * < 0.05.
Sustainability 17 02830 g001
Table 1. Results of the path model.
Table 1. Results of the path model.
Remote Work
Overload		Role ClarityCoworker SupportBurnout
BSEp-ValueBSEp-ValueBSEp-ValueBSEp-Value
Constant Controls−0.734  1.846   2.851  0.386***  1.306  0.494**    3.215  0.408***
Sex (Female)−0.4730.33   0.084  0.069   0.154  0.088 −0.09  0.069
Age  0.005  0.016   0.006  0.003 −0.004  0.004  −0.008  0.003*
Race (White)−0.419  0.383 −0.1470.08 0.11  0.103  −0.0920.08
Education Year (6–20)  0.438  0.107***−0.069  0.022**  0.024  0.029    0.047  0.023*
Income−0.3120.11**  0.065  0.023**  0.0810.03** −0.076  0.023**
Tenure Status (Employees not manager)−0.984  0.351**  0.025  0.073   0.187  0.094*   0.033  0.074
Years in an Organization  0.001  0.017   0.001  0.004   0.006  0.005   0.004
Change in Working Hour0.02  0.016   0.005  0.003 −0.002  0.004  −0.002  0.003
CT use with Coworkers
Phone Call (0–4)  0.044  0.159   0.138  0.033***  0.056  0.043  −0.072  0.034*
Video Call (0–4)  0.658  0.156***−0.004  0.033 −0.064  0.042 −0.01  0.033
Mobile Text (0–4)  0.267  0.154 −0.008  0.032   0.061  0.041   0.05  0.032
Email (0–4)−0.3730.19   0.0880.04*  0.048  0.051  −0.0390.04
Instant Message (0–4)  0.133  0.141   0.0410.03   0.104  0.038**   0.0210.03
Job Resource/Demand
Remote Work Overload (0–4)    0.0760.01***
Role Clarity (0–4)  −0.391 0.053***
Coworker support (0–4)  −0.144 0.041***
R-Square0.1620.1340.0800.406
B: unstandardized coefficient; *** < 0.001, ** < 0.01, and * < 0.05.
Table 2. The indirect relationship in the path model.
Table 2. The indirect relationship in the path model.
Indirect RelationshipsBSELLCIULCI
Phone Call → Role Clarity → Burnout−0.0540.015−0.088−0.027
Email → Role Clarity → Burnout−0.0340.018−0.071  −0.0003
Instant Message → Emotional Support → Burnout−0.0160.008−0.031−0.002
Video Call→ Remote work overload → Burnout  0.0500.014  0.024  0.081
Only significant relationships are indicated.
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MDPI and ACS Style

Shin, I.; Riforgiate, S.E.; Godager, E.A.; Coker, M.C. Remote Worker Communication Technology Use Related to Role Clarity, Coworker Support, and Work Overload. Sustainability 2025, 17, 2830. https://doi.org/10.3390/su17072830

AMA Style

Shin I, Riforgiate SE, Godager EA, Coker MC. Remote Worker Communication Technology Use Related to Role Clarity, Coworker Support, and Work Overload. Sustainability. 2025; 17(7):2830. https://doi.org/10.3390/su17072830

Chicago/Turabian Style

Shin, Inyoung, Sarah E. Riforgiate, Emily A. Godager, and Michael C. Coker. 2025. "Remote Worker Communication Technology Use Related to Role Clarity, Coworker Support, and Work Overload" Sustainability 17, no. 7: 2830. https://doi.org/10.3390/su17072830

APA Style

Shin, I., Riforgiate, S. E., Godager, E. A., & Coker, M. C. (2025). Remote Worker Communication Technology Use Related to Role Clarity, Coworker Support, and Work Overload. Sustainability, 17(7), 2830. https://doi.org/10.3390/su17072830

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